Analysis part I: startup’s distinctiveness

Model 0 only reports the estimated log odds for the control variables and serves as a baseline. The results for the major control variables are as expected. The greater the industry distance between a startup and an investor, the lower the probability of an investment is. The coefficient for geographical distance is also as expected, negative and strongly significant, reducing the probability of an investment. The same is true for our two measures of affiliate distance: The greater the industry and geographical distance of the closest syndicate partner, the lower the investment proba-bility. It is important to note that the affiliate geographical distance is de-fined as the inverse of geographical distance, so that a positive value in the regression model indicates lower probability with larger distance. Inter-estingly, the effect on log odds is significantly larger for affiliate industry distance than for the direct industry distance, indicating that the syndicate distance is even more important than the direct distance. Furthermore, an

investor who has previously invested in the startup increases the probabil-ity of an investment with strong significance. We observe the largest coef-ficient for this control variable. Control variables that are only dependent on either the venture capitalist, for example, age or industry experience, or only dependent on the startup, for example, amount raised or geograph-ical location, cannot be directly interpreted in our research design. As all of the startups in our sample received funding in that quarter, it would be wrong to interpret the coefficients as the log odds of receiving funding. We can only derive conclusions for variables that affect the venture capitalist - startup dyad. In other words, only the main effects of variables that si-multaneously depend on the venture capitalist and the startup, such as any distance measure, can be meaningfully interpreted. That is also the reason why we have not included a hypothesis on the main effect of the startup’s distinctiveness on the probability of receiving funding.

In model 1 we added the four independent variables from the first part of our analysis: the startup’s distinctiveness, the portfolio distinctiveness, the portfolio diversification, and the venture capitalist’s status. As outlined above, we cannot interpret the coefficients, as they are affected by the dis-tribution of our sample but do not describe a dyad characteristic. With the following models, we test which investors prefer a startup’s distinctiveness.

Model 2 incorporates the venture capitalist’s historical average portfolio distinctiveness and the interaction effect with the startup’s distinctiveness.

As expected, we found strongly significant evidence for our hypothesis that venture capitalists who have previously invested in distinctive startups and gained experience in managing the associated risks have a higher probabil-ity of investing in a distinctive startup. Figure 6.8a clearly supports this hypothesis. In the plot, the solid line depicts the relationship of a startup’s distinctiveness with the probability of investment in the case of portfolio distinctiveness two standard deviations below the mean portfolio distinc-tiveness. The line’s negative slope is to interpreted as a decreasing proba-bility of investment when the a startup’s distinctiveness increases for ven-ture capitalists with low portfolio distinctiveness. The dotted line for two standard deviations above the mean of portfolio distinctiveness is upward sloping, which means that, for venture capitalists with high portfolio dis-tinctiveness, the probability of investment increases with an increase in a startup’s distinctiveness.

Model 3 adds to model 1 the portfolio diversification of the focal ven-ture capitalist and its interaction effect with the startup’s distinctiveness.

We have expected a negative effect on the probability of investing in a dis-tinctive startup, but we cannot confirm this expectation. Rather, we observe a weakly significant and positive coefficient of the interaction effect indicat-ing that investors who try to diversify their portfolios in order to reduce risk

prefer distinctive new ventures. The graph in figure 6.8b supports this find-ing with an upward-slopfind-ing dotted line for highly diversified investors and a downward-sloping solid line for venture capitalists with lowly diversified portfolios.

In model 4, we have added the status measure based on venture cap-italists’ historical syndicate networks and the respective interaction effect.

We find weakly significant support for the hypothesis that with the increas-ing centrality of a venture capitalist, the appetite for distinctive startups increases. Again, the slopes in figure 6.8c show graphical support.

In model 5 we introduce all the interaction effects for our analysis of startup’s distinctiveness, the portfolio characteristics, and the venture cap-italist’s status. The direction of the interaction effects’ coefficients remains the same but the previously weakly significant results for the interaction with portfolio diversification and status are no longer significant at the 5%

level.

Chapter6.Results

(0.35) (0.36) (0.38) (0.37) (0.35) (0.40)

State dummy CA −0.26^∗∗∗ −0.25^∗∗∗ −0.26^∗∗∗ −0.26^∗∗∗ −0.25^∗∗∗ −0.25^∗∗∗

(0.03) (0.03) (0.03) (0.03) (0.03) (0.03)

State dummy MA −0.23^∗∗∗ −0.22^∗∗∗ −0.23^∗∗∗ −0.22^∗∗∗ −0.23^∗∗∗ −0.22^∗∗∗

(0.04) (0.04) (0.04) (0.04) (0.04) (0.04)

State dummy NY −0.15^∗∗∗ −0.13^∗∗∗ −0.15^∗∗∗ −0.14^∗∗∗ −0.14^∗∗∗ −0.13^∗∗

(0.04) (0.04) (0.04) (0.04) (0.04) (0.04)

Startup age 0.00 0.00 0.00 0.00 0.00 0.00

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Raised amoung (log) 0.06^∗∗∗ 0.07^∗∗∗ 0.07^∗∗∗ 0.07^∗∗∗ 0.07^∗∗∗ 0.07^∗∗∗

(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

Geographical distance (log) −0.17^∗∗∗ −0.17^∗∗∗ −0.17^∗∗∗ −0.17^∗∗∗ −0.17^∗∗∗ −0.17^∗∗∗

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Geographical distance no data dummy 0.36^∗∗∗ 0.35^∗∗∗ 0.36^∗∗∗ 0.40^∗∗∗ 0.31^∗∗∗ 0.35^∗∗∗

(0.06) (0.06) (0.06) (0.06) (0.06) (0.06)

Industry distance −1.52^∗∗∗ −1.61^∗∗∗ −1.53^∗∗∗ −1.68^∗∗∗ −1.46^∗∗∗ −1.60^∗∗∗

(0.07) (0.07) (0.07) (0.07) (0.07) (0.07)

Prior investment dummy 5.84^∗∗∗ 5.80^∗∗∗ 5.83^∗∗∗ 5.83^∗∗∗ 5.81^∗∗∗ 5.79^∗∗∗

(0.17) (0.17) (0.17) (0.17) (0.17) (0.17)

Mean affiliation 0.04^∗∗∗ 0.05^∗∗∗ 0.04^∗∗∗ 0.04^∗∗∗ 0.05^∗∗∗ 0.05^∗∗∗

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Affiliate geographical distance 0.01^∗∗∗ 0.01^∗∗∗ 0.01^∗∗∗ 0.01^∗∗∗ 0.01^∗∗∗ 0.01^∗∗∗

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Affiliate industry distance −3.73^∗∗∗ −3.78^∗∗∗ −3.73^∗∗∗ −3.70^∗∗∗ −3.81^∗∗∗ −3.78^∗∗∗

(0.08) (0.09) (0.08) (0.08) (0.09) (0.09)

VC age 0.00 −0.00 0.00 0.00 −0.00 −0.00

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

General experience −0.00^∗∗∗ −0.00^∗∗∗ −0.00^∗∗∗ −0.00^∗∗∗ −0.00^∗∗ −0.00^∗∗∗

Modelresults117

Industry experience 0.01^∗∗∗ 0.02^∗∗∗ 0.01^∗∗∗ 0.01^∗∗∗ 0.01^∗∗∗ 0.02^∗∗∗

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Startup’s distinctiveness −0.16^∗ −1.51^∗∗∗ −0.64^∗∗ −0.28^∗∗∗ −1.78^∗∗∗

(0.06) (0.32) (0.23) (0.09) (0.38)

Portfolio distinctiveness −0.14 −1.45^∗∗∗ −1.26^∗∗∗

(0.13) (0.33) (0.34)

Portfolio diversification 0.03^∗∗∗ 0.02^∗∗ 0.02^∗∗

(0.00) (0.01) (0.01)

VC status −0.14^∗∗∗ −0.17^∗∗∗ −0.16^∗∗∗

(0.01) (0.01) (0.01)

Startup’s distinctiveness x Portfolio distinctiveness 2.87^∗∗∗ 2.42^∗∗∗

(0.66) (0.67)

Startup’s distinctiveness x Portfolio diversification 0.02^∗ 0.02

(0.01) (0.01)

Startup’s distinctiveness x VC status 0.06^∗ 0.05

(0.02) (0.02)

AIC 49,559 49,149 49,540 49,401 49,284 49,131

BIC 50,106 49,731 50,113 49,975 49,858 49,741

Log Likelihood -24,719 -24,509 -24,706 -24,637 -24,578 -24,498

Deviance 49,437 49,019 49,412 49,273 49,156 48,9956

Num. obs. 58,000 58,000 58,000 58,000 58,000 58,000

∗∗∗p <0.001,^∗∗p <0.01,^∗p <0.05

?The table shows the results of the logistic regression models with correction of coefficients for rare events. Standard errors are shown in parentheses.

Year dummies, round number dummies, and investor count dummies are included in all models. The unit of analysis is the venture capitalist - startup funding round dyad and the sample includes 58,000 observations from the period 2005 - 2015. The observations are half realized investments and half unrealized investments. The dependent variabledummy actual datais binary and equals 1 when the observation is a realized investment.

TABLE6.4: Logistics regression output models part I: startup’s distinctiveness^?